Methods and systems for acquiring user related information using natural language processing techniques

ABSTRACT

Systems and methods for acquiring information associated with a user by using NLP techniques are disclosed. One or more phrases are classified in one or more categories at least partly on the basis of a period for which a product has been used by the user, the user&#39;s experience with the product, preferences of the user, or needs of the user by applying one or more natural language processing (NLP) techniques. The one or more phrases are extractable from an electronic publication at least partly on the basis of on a predefined set of verbs, a predefined set of domain-specific terms, and terms indicative of temporal information. One or more terms from the classified phrases are extracted, in which the one or more terms are indicative of the information about the user.

TECHNICAL FIELD

The presently disclosed embodiments are related, in general, to a datamining system. More particularly, the presently disclosed embodimentsare related to systems and methods for acquiring information about auser by using (NLP) techniques.

BACKGROUND

A typical user profiling system collects information related to a userto create a user profile. Such information may include, but not limitedto, name, sex, hobbies, area of interest, and the like. In one scenario,the user profiling system may obtain such information from the user. Inanother scenario, the user profiling system may obtain such informationabout the user by tracking or monitoring activities performed by theuser on a daily basis. For example, the user profiling system tracksuser activity (e.g., the user's web browsing pattern) by monitoringcookies associated with the websites accessed by the user. For instance,the user may frequently visit a website to search and buy latestelectronic gadgets. The user profiling system may track the cookies todetermine that the user is interested in electronic gadgets.Accordingly, the user profiling system may create or update the userprofile.

SUMMARY

According to embodiments illustrated herein, there is provided a methodof acquiring information about a user, the method includes classifyingone or more phrases in one or more categories at least partly on thebasis of a period for which a product has been used by the user, theuser's experience with the product, preferences of the user, or needs ofthe user by applying one or more natural language processing (NLP)techniques. The one or more phrases are extractable from an electronicpublication based, at least in part, on a predefined set of verbs, apredefined set of Domain-specific terms, and terms indicative oftemporal information. Further, the method includes extracting one ormore terms form the classified phrases. The one or more terms areindicative of the information about the user.

According to embodiments illustrated herein, there is provided a methodof providing one or more services to a user. The method includesclassifying one or more phrases in one or more categories at leastpartly on the basis of a period for which a product has been used by theuser, the user's experience with the product, preferences of the user,or needs of the user by applying one or more natural language processing(NLP) techniques. The one or more phrases are extractable from anelectronic publication based, at least in part, on a predefined set ofverbs, a predefined set of Domain-specific terms, and terms indicativeof temporal information. The method further includes extracting one ormore terms form the classified phrases. The one or more terms areindicative of the information about the user. Further more the methodincludes creating a user profile based on the classified phrases.Finally, the method includes providing the one or more services to theuser based on the user profile. The one or more services correspond toproduct support, product recommendation, and troubleshooting.

According to embodiments illustrated herein, there is provided a systemfor creating a user profile. The system includes a search moduleconfigured to search for an electronic publication on one or more onlinesources. A natural language processing (NLP) module configured toextract one or more phrases from the electronic publication based, atleast in part, on a predefined set of verbs, a predefined set ofDomain-specific terms, and terms indicative of temporal information. TheNLP module is further configured to classify the one or more phrases inone or more categories at least partly on the basis of a period forwhich a product has been used by the user, the user's experience withthe product, preferences of the user, or needs of the user. A userprofile manager configured to create the user profile based on theclassified phrases.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings illustrate various embodiments of systems,methods, and other aspects of the disclosure. Any person having ordinaryskill in the art will appreciate that the illustrated element boundaries(e.g., boxes, groups of boxes, or other shapes) in the figures representone example of the boundaries. It may be that in some examples, oneelement may be designed as multiple elements or that multiple elementsmay be designed as one element. In some examples, an element shown as aninternal component of one element may be implemented as an externalcomponent in another, and vice versa. Furthermore, elements may not bedrawn to scale.

Various embodiments will hereinafter be described in accordance with theappended drawings, which are provided to illustrate, and not to limitthe scope in any manner, wherein like designations denote similarelements, and in which:

FIG. 1 is a block diagram illustrating a system environment, in whichvarious embodiments can be implemented;

FIG. 2 is a flowchart illustrating a method of acquiring informationabout a user in accordance with at least one embodiment;

FIG. 3 is a snapshot illustrating a portion of an electronic publicationin accordance with at least one embodiment; and

FIG. 4 is block diagram of an analytic server in accordance with atleast one embodiment.

DETAILED DESCRIPTION

The present disclosure is best understood with reference to the detailedfigures and description set forth herein. Various embodiments arediscussed below with reference to the figures. However, those skilled inthe art will readily appreciate that the detailed descriptions givenherein with respect to the figures are simply for explanatory purposesas the methods and systems may extend beyond the described embodiments.For example, the teachings presented and the needs of a particularapplication may yield multiple alternate and suitable approaches toimplement the functionality of any detail described herein. Therefore,any approach may extend beyond the particular implementation choices inthe following embodiments described and shown.

References to “one embodiment”, “an embodiment”, “one example”, “anexample”, “for example” and so on, indicate that the embodiment(s) orexample(s) so described may include a particular feature, structure,characteristic, property, element, or limitation, but that not everyembodiment or example necessarily includes that particular feature,structure, characteristic, property, element or limitation. Furthermore,repeated use of the phrase “in an embodiment” does not necessarily referto the same embodiment.

DEFINITIONS

The following terms shall have, for the purposes of this application,the respective meanings set forth below.

A “user profile” refers to an electronic collection of informationassociated with a user. In an embodiment, such information may include,but not limited to, name, age, sex, hobbies, user's interests, user'spreferences, user's needs, and so forth.

A “customer support center” refers to a system that provides one or moreservices to a user. In an embodiment, the user sends a query to thecustomer support center asking for information on a product. Thecustomer support center provides the information of the product. In anembodiment, the customer support center may provide one or more servicesto the user based on the query. For example, the customer care centermay recommend one or more similar products to the user. In anembodiment, the one or more service includes, but is not limited to,product support, product recommendation, and troubleshooting.

A “phrase” refers to a sequence of two or more words arranged in agrammatical construction and acting as a unit in a sentence.

“Categories” refer to one or more groups, in which one or more phrasesare be classified based on the context of the one or more phrases. In anembodiment, the categories include user's preferences, needs, andexperience.

An “electronic publication” refers to one or more articles published bya user. The one or more articles may include, but not limited to, ane-mail, a post on a social networking website, a post on a blog, and thelike. In an embodiment, the electronic publication includes the user'sreview on a product, user's needs, and user's preferences.

A “product” refers to an object that a user can buy or own. Someexamples of the product may include, but not limited to, device, apolicy, a bond, shares, and the like.

FIG. 1 is a block diagram illustrating a system environment 100, inwhich various embodiments can be implemented. The system environment 100includes an analytic server 102, a product database 104, a user database106, a network 108, and a customer support center 110. The analyticserver 102 further includes a natural language processing (NLP) module112.

The analytic server 102 searches for one or more electronic publicationson one or more online sources. Further, the analytic server 102 includesthe NLP module 112 that analyzes the one or more electronicpublications. The NLP module 112 extracts one or more phrases from theone or more electronic publications. Thereafter, the NLP module 112classifies each of the one or more phrases in one or more categories byapplying the first set of rules. Based on the classified phrases, theNLP module 112 extracts the one or more terms from each of theclassified phrases by applying the second set of rules. In anembodiment, some examples of the NLP techniques implemented by the NLPmodule 112 includes, but not limited to, word tokenization, wordlemmatization, part-of-speech tagging, Named Entity detection, syntacticparsing. Thereafter, the analytic server 102 creates the user profilebased on the one or more terms. The analytic server 102 stores the userprofile in the user database 106. In an embodiment, the analytic server102 includes one or more analytic tools, such as the NLP module 112,which further includes a natural language parser and a part of speech(POS) tagger. The analytic server 102 can be realized through varioustechnologies such as Apache® web server and Microsoft® web server. Theanalytic server 102 is described in detail in conjunction with FIG. 4.

The product database 104 includes information related to variousproducts. In an embodiment, the information may include, but not limitedto, model number, price, features, and user reviews on the product. Inan embodiment, the product database 104 may receive a query to extractinformation related to a product. In an embodiment, the product database104 may receive the query from the customer support center 110. In analternate embodiment, the product database 104 may receive the queryfrom the analytic server 102. The product database 104 may be realizedthrough various technologies such as, but not limited to, Microsoft® SQLserver, My SQL, and ODBC server.

The user database 106 is a repository of the user profiles. The analyticserver 102 creates and updates the user profiles in the user database106. The user database 106 may be realized through various technologiessuch as, but not limited to, Microsoft® SQL server, My SQL, and ODBCserver.

The network 108 corresponds to a medium through which the content andthe messages flow between various components (e.g., the analytic server102, the product database 104, the user database 106, and the customersupport center 110) of the system environment 100. Examples of thenetwork 108 may include, but are not limited to, a Wireless Fidelity(WiFi) network, a Wireless Area Network (WAN), a Local Area Network(LAN), and a Metropolitan Area Network (MAN). Various devices in thesystem environment 100 can connect to the network 108 in accordance withvarious wired and wireless communication protocols, such as TransmissionControl Protocol and Internet Protocol (TCP/IP), User Datagram Protocol(UDP), 2G, 1G or 4 G communication protocols.

The customer support center 110 receives one or more requests/query froma user. In an embodiment, the customer support center 110 receives thequery regarding a product. In an embodiment, the query received by thecustomer support center corresponds to a question asked by the userabout a product. In an embodiment, the user may send the query in formof, but not limited to, an e-mail, voice call, Short Message Service(SMS), or any other means of transmitting the query.

On receiving the query, the customer support center 110 accesses theuser database 106 to extract user profile associated with the user. Ifthe user profile is present in the user database 106, the customersupport center 110 extracts the user profile. If the user profile is notpresent, the customer support center 110 sends a request to the analyticserver 102 to create the user profile. In an embodiment, a customer careexecutive at the customer support center 110 interacts with the userbased on the information available in the user profile. In anembodiment, the customer care executive recommends one or more productsto the user based on the user profile. In an alternate embodiment, theanalytic server 102 provides one or more services to the user based onthe user profile. For example, post the creation of the user profile,the analytic server 102 extracts one or more product information fromthe product database 104. Thereafter, the analytic server 102 recommendsthe one or more products to the user without manual intervention of thecustomer care executive at the customer support center 110. The analyticserver 110 may provide the one or more services to the user over anE-mail, Interactive voice response IVR system, or FAX.

FIG. 2 is a flowchart 200 illustrating a method of acquiring informationabout a user in accordance with at least one embodiment. The flowchart200 is described in conjunction with FIG. 1.

The customer support center 110 receives a query pertaining to anyrequirement from a user. In an embodiment, the query may be in the formof an E-mail or a voice-call. On receiving the query, the customersupport center 110 accesses the user database 106 to extract the userprofile associated with the user to better assist the user. If the userprofile is present in the user database 106, the customer support center110 extracts the user profile. If the user profile is not present, thecustomer support center 110 sends a request to the analytic server 102to create the user profile.

At step 202, one or more electronic publications are searched on one ormore online sources. In an embodiment, the search for the electronicpublications is performed by the analytic server 102 in response therequest by the customer support center 110. On receiving the request tocreate the user profile, the analytic server 102 acquires informationabout the user by searching for the electronic publications posted bythe user. For example, the user posts a comment on a blog stating, “ABCprinter has a speed of 12 ppm”. The analytic server 102 considers such apost as the electronic publication. In an embodiment, the one or moreonline sources may correspond to a product review website, a forum, ablog, or an E-mail.

At step 204, one or more phrases are extracted from the electronicpublications. In an embodiment, the NLP module 112 extracts the one ormore phrases from the electronic publications. The NLP module 112includes a natural language parser that separates various parts ofspeech (POS) in a sentence. The natural language parser is executedthrough each sentence in the electronic publications to extract one ormore phrases. The natural language parser utilizes a word-list databaseto extract the one or more phrases. In an embodiment, the word-listdatabase includes a predefined set of verbs, a predefined set ofdomain-specific terms, and terms indicative of temporal information, andthe like. Further, the natural language parser utilizes various onlinesources including, but not limited to, Dictionary.com®, Thesaurus®, andWordWeb®, to determine synonyms for the words in the word-list database.An example of the word-list database is described below in conjunctionwith Table 1.

TABLE 1 Example word-list database Verbs Temporal terms Negation termsDomain-specific terms Buy Year Don't Printer Purchase Late Never ScannerOwn Month Avoid FAX Need Date MFD

For example, the natural language parser analyzes a sentence in anelectronic publication that recites, “I bought ABC printer in 2002”.Firstly, the natural language parser determines various parts of speechin the sentence, i.e., subject, verb, and object. The natural languageparser classifies “I” as subject, “bought” as verb, and “ABC printer” asobject. Thereafter, the natural language parser compares each part ofspeech extracted from the sentence with the word-list database (Table 1)to determine whether the phrase is relevant. As the sentence includesterms “bought” (i.e., past tense of term “buy”) and “ABC printer” (i.e.,domain-specific term), the sentence is relevant. Finally, the naturallanguage parser extracts the phrase “I bought ABC printer in 2002” fromthe electronic publication.

A person having ordinary skill in the art would understand that theterms mentioned in the table 1 may further include synonyms of the termsand various verb-forms variations of the terms without departing fromthe scope of the disclosed embodiments.

At step 206, the one or more phrases are classified under one or morecategories, based on the context of each of the one or more phrases. Inan embodiment, the NLP module 112 categorizes the one or more phrases inone or more categories on the basis of the context of the phrases. In anembodiment, the one or more categories include, but are not limited to,user's experience, user's preferences, and user's needs. In order todetermine the context of the one or more phrases, the NLP module 112maintains a semantically classified second word-list database and afirst set of rules. The semantically classified second word-listdatabase is described below in conjunction with Table 2.

TABLE 2 Semantically classified second word-list database PossessionExperience with Needs Need of user Preference of of product product ofuser (implicit) users Buy Previous Need Because of Never Had Before UseFor Don't Own In the past Intentions Avoid Have Want Like Desire

In an embodiment, any phrase that includes the terms mentioned in thecolumn titled “Possession of product” may signify that the user owns aproduct. Similarly, any phrase that includes the terms mentioned in thecolumn titled “Needs of user” may signify the user's requirements orexpectations.

A person having ordinary skill in the art would understand that theterms mentioned in table 2 may further include synonyms of the terms andvarious verb-forms variations of the terms without departing from thescope of the disclosed embodiments.

In order to classify the one or more phrases in the one or morecategories, the NLP module 112 applies a first set of rules on each ofthe one or more phrases. In an embodiment, for a phrase to classifyunder the category “user's experience”, the phrase should qualify thefollowing rules:

If (Subject==(“Possession of product”, (pronoun)) andObject==(“Domain-specific terms” (mentioned in table 1)) then (classifythe phrase in “user's experience”)  (1)

If (Subject==(“Experience with product”, (pronoun)) andObject==(“Domain-specific terms” (mentioned in table 1)) then (classifythe phrase in “user's experience”).  (2)

The rule (1) states that any phrase whose subject portion includes termsmentioned in the column titled “possession of product” (refer Table 2)and has an object portion that includes terms mentioned in the columntitled “Domain-specific terms” (refer Table 1) is classified in theuser's experience category. Similarly, the rule (2) states that anyphrase whose subject portion includes terms mentioned in the columntitled “Experience with product” (refer Table 2) and has an objectportion that includes terms mentioned in the column titled“Domain-specific terms” (refer Table 1) is classified in the user'sexperience category.

In an embodiment, for a phrase to classify under the category “user'sneeds”, the phrase should qualify the following rules:

If (Subject==(“Needs of user”, (pronoun)) and Object==(“Domain-specificterms” (mentioned in table 1)) then (classify the phrase in “user'sneed”)  (3)

If (Subject==(“Possession of product”, (pronoun)) and Object==(“Need ofuser (implicit)”, “other terms”) then (classify the phrase in “user'sneed”)  (4)

The rule 3 states that any phrase whose subject portion includes termsmentioned in column “Needs of user” of Table 2 and has an object portionthat includes terms mentioned in column “Domain-specific terms” of Table1 is classified in the user's needs category. The Rule 4 states that anyphrase whose subject portion includes terms mentioned in column“possession of product” of Table 2 and has an object portion thatincludes terms mentioned in columns “needs of the user (implicit)” ofTable 2 and “other terms” of Table 1 is classified in the user's needscategory.

In an embodiment, for a phrase to classify under the category “userpreference”, the phrase should qualify the following rule:

If (Subject==(“preference of user”, (pronoun)) andObject==(“Domain-specific terms” (mentioned in table 1)) then (classifythe phrase in “user's preference”)  (5)

Rule (5) states that any phrase whose subject portion includes termsmentioned in the column titled “preference of user” (refer Table 2) andhas an object portion that includes terms mentioned in the column titled“Domain-specific terms” (refer Table 1) is classified in the userpreference category.

By applying the first set of rules (e.g., rules 1-5), the NLP module 112categorizes the one or more phrases under the one or more categories asillustrated in Table 3 below.

TABLE 3 Classified phrases User's experience User's needs User'spreferences “I bought XXXX “I need an “I don't like ZZZZ printer in2005” integrated scanner, printer” printer, and copier”

A person having ordinary skills in the art would understand that theabove mentioned rules have been illustrated as an example. Various othertypes of grammatical, as well as syntactical rules can be applied to theone or more phrases without limiting the scope of the ongoingdescription.

At step 208, one or more terms are extracted from the classifiedphrases. In an embodiment, the NLP module 112 extracts the one or moreterms from the classified phrases. In an embodiment, the NLP module 112applies a second set of rules on the classified phrases to extract theone or more terms. In an embodiment, the one or more terms areindicative of the information about the user. In an embodiment, thesecond set of rules is applied to the classified phrases in each of theone or more categories. Following is an example of a rule applied on thephrase in the user's experience category:

If (Subject==(“Possession of product”, (pronoun)) andObject==(“Domain-specific terms” (mentioned in table 1)) then (extracttemporal term)  (6)

Experience with the product=current date−temporal term  (7)

The rule 6 states that a temporal term is extracted from the phraseclassified under the user's experience category if the subject portionof the classified phrase includes term mentioned in column “Possessionof product” of Table 2 and the object portion of the phrase includesterms mentioned in column “Domain-specific terms” of Table 1. The totalyears of experience are calculated using the equation 7.

Following is an example of a rule applied on phrases in the user's needscategory:

If (Subject==(“Needs of user”, (pronoun)) and Object==(“Domain-specificterms” (mentioned in table 1)) then (extract other terms)  (8)

Rule 8 states that domain-specific terms are extracted from a phraseclassified under the user's needs category if the subject portion of theclassified phrase includes term mentioned in the column “Needs of user”of Table 2 and the object portion of the phrase includes terms mentionedin column “Domain-specific terms” of Table 1. In an embodiment, thedomain-specific terms, extracted using the rule 8, correspond to theneeds of the user.

Following is an example of a rule applied on phrases in the user's needscategory:

If (Subject==(“preference of user”, (pronoun)) andObject==(“Domain-specific terms” (mentioned in table 1)) then (extractother terms)  (9)

Rule 9 states that the domain-specific terms are extracted from a phraseclassified under the user's needs category if the subject portion of theclassified phrase includes the terms mentioned in column “Preference ofuser” of Table 2 and the object portion of the phrase includes termsmentioned in column “other terms” of Table 1. In an embodiment, thedomain-specific terms, extracted using the rule 9, correspond to theuser's preferences.

At step 210, a user profile is created based on the one or more termsextracted from the classified phrases by applying the second set ofrules (e.g. rules 6-9). In an embodiment, the analytic server 102creates the user profile. In an embodiment, the user profile includesinformation about the user's needs, user's preferences, and user'sexperience. A sample user profile is illustrated in Table 4 below.

TABLE 4 Example user profile Name User-1 Age 30 Experience XXXX printer7 years Needs Integrated scanner, printer, copier Preferences Avoid ZZZZprinters

At step 212, the user profile is communicated to the customer supportcenter 110. In an embodiment, the analytic server 102 communicates theuser profile to the customer support center 110. At the customer supportcenter 110, the user profile is analyzed to determine the needs andpreferences of the user. Based on the determined needs and preferences,one or more services are provided to the user. In an embodiment, the oneor more services include, but are not limited to, product support,product recommendation, and troubleshooting. For example, the userprofile states that the user needs a standalone FAX machine and a colorprinter. Further, the user profile states that the user does not likeZZZZ printer. Based on the user's needs and user's preferences, thecustomer support center 110 may generate a query to extract theinformation about one or more products that includes the standalone FAXand color printer. In an alternate embodiment, the analytic server 102generates the query. An example query is mentioned below.

-   -   Select “product name” and “features”;    -   From “product database 104”;        where (features=“standalone FAX machine” and “color printer”)        and (NOT product name=BBBB printer).

The customer care executive at the customer support center 110 mayutilize the extracted product information to recommend one or moreproducts to the user.

It is understood by a person having ordinary skill in the art that thescope of the disclosure should not be limited to creating the userprofile using the electronic publications. In an embodiment, a querysent to the customer support center 110 over the voice call can beutilized for extracting information required to create the user profile.The voice call is converted to text using one or more speech-to-text(STT) techniques. Thereafter, the one or more phrases are extracted fromthe converted text using the method illustrated in the flowchart 200.

Further, it is also understood by a person having ordinary skill in theart that the scope of the invention should not be limited torecommending products such as scanner and printers to the user. Variousother products, such as shares, bonds, and insurance policies, can berecommended to the users. In such a case, the domain-specific termsmentioned in Table 1 would vary in accordance with other products. Forexample, domains-specific terms for the domain of “insurance” wouldinclude interest rates, maturity period, principle amount, and the like.

FIG. 3 is a snapshot illustrating a portion of an electronic publication300 in accordance with at least one embodiment. FIG. 3 is described inconjunction with FIG. 1 and FIG. 2.

The analytic server 102 extracts the electronic publication 300 from oneor more online sources. The natural language parser in the NLP module112 parses each sentence in the publication. For example, the naturallanguage parser parses the sentence “I bought the XXXX printer in 2005”(depicted by 302) to classify “I” as subject, “bought” as verb, and“XXXX printer” as object. Similarly, the natural language parser parsesthe sentence “I started having trouble with this printer” (depicted by308) to classify “I” as subject, “started having” as verb, and “thisprinter” as object. Thereafter, the natural language parser compares thewords in the sentence 302 and the sentence 308 to determine whether thesentences 302 and 308 are relevant. As the words in the sentence 308 arenot present in the word-list database (as shown in Table 1), thesentence 308 is considered as irrelevant. The natural language parserextracts one or more relevant phrases from the publication as describedin the step 204.

Thereafter, the NLP module 112 classifies each of the extracted phrasesinto one or more categories based on the context of the extractedphrases. In an embodiment, The NLP module 112 applies the rules 1 to 5on each of the extracted phrases to classify the one or more phrases into the one or more categories. For example, the NLP module 112 appliesthe first set of rules on phrase 302 that states, “I bought the XXXXprinter in 2005”. By applying the rule 1 on the phrase 302, the NLPmodule 112 observes that the subject portion of the phrase includes theterm “bought” (i.e., past tense of the term “buy” mentioned in the“possession of product” column in Table 2) and the object portionincludes term “XXXX printer” (mentioned in the column “Domain-specificterms” in Table 1). Thus, the NLP module 112 classifies the phrase 302under the category “user's experience”. In another example, the NLPmodule 112 applies the first set of rules to the phrase 304 that states,“I needed standalone FAX and flatbed scanning”. By applying the rule 3on the phrase 304, the NLP module 112 observes that the subject portionof the phrase 304 includes the term “needed” (i.e., past tense of theterm “need” mentioned in column “needs of user” in Table 2) and theobject portion includes terms “Flatbed Scanning and standalone FAX”(mentioned in column “Domain-specific terms” in Table 1). Thus, the NLPmodule 112 classifies the phrase 304 under the user's experiencecategory. Similarly, the NLP module 112 applies the first set of rulesto each of the extracted phrases to obtain a category-wise distributionof the extracted phrases. The category-wise classification of theextracted phrases has been illustrated below in Table 5.

TABLE 5 Classified phrases User's Experience User's Needs User'sPreferences I bought the XXXX printer I needed standalone I would neverin 2005 (depicted by 302) FAX and flatbed buy a BBBB scanning productagain (depicted by 304) (depicted by 312) My previous experience I usedit mainly for I will never buy of such printers was with color printinganother BBBB product a YYYY model (depicted (depicted by 306) (depictedby 314) by 310)

Subsequently, the analytic server 102 creates the user profile based onthe classified sentences. To create the user profile, the NLP module 112applies the second set of rules to the classified phrases to extract oneor more terms from each of the classified phrases. For example, The NLPmodule 112 applies the rules 6 and 7 on the phrase 302 to determine thatthe user has been using the XXXX printer for seven years. Further, theNLP module 112 extracts such information from each of the classifiedphrases by applying the second set of rules. This information isutilized by the analytic server 102 to create the user profile asillustrated below in Table 6.

TABLE 6 Example user profile Name User-1 Experience 1. XXXX printer for7 years 2. Previous experience with YYYY model Needs 1. Standalone FAXand flatbed scanning 2. Color printing Preferences Avoid BBBB product

FIG. 4 is a block diagram of the analytic server 102 in accordance withat least one embodiment. The analytic server 102 includes a processor402, a transceiver 404, and a memory 406. The analytic server 102 isdescribed in conjunction with FIG. 1 and FIG. 2.

The processor 402 is coupled to the transceiver 404 and the memory 406.The processor 402 executes a set of instructions stored in the memory406. The processor 402 can be realized through a number of processortechnologies known in the art. Examples of the processor 402 can be, butare not limited to, X86 processor, RISC processor, ASIC processor, andCISC processor.

The transceiver 404 transmits and receives messages and data to/from thevarious components (e.g., the product database 104, the user database106, and the customer support center 110) of the system environment 100(refer FIG. 1). Examples of the transceiver 404 can include, but are notlimited to, an antenna, an Ethernet port, a USB port, or any port thatcan be configured to receive and transmit data from external sources.The transceiver 404 transmits and receives data/messages in accordancewith various communication protocols, such as, Transmission ControlProtocol and Internet Protocol (TCP/IP), USB, User Datagram Protocol(UDP), 2G, 3G and 4 G communication protocols.

The memory 406 stores a set of instructions and data. Some of thecommonly known memory implementations can be, but are not limited to,random access memory (RAM), read only memory (ROM), hard disk drive(HDD), and secure digital (SD) card. The memory 406 includes a programmodule 408 and a program data 410. The program module 408 includes a setof instructions that can be executed by the processor 402 to perform oneor more operations on the analytic server 102. The program module 408includes a communication manager 412, a search module 414, the NLPmodule 112, a user profile manager 416, a product database manager 418,and a customer care manager 420. Although the various modules in theprogram module 408 have been shown in separate blocks, one or more ofthe modules may be implemented as an integrated module performing thecombined functions of the constituent modules.

The program data 410 includes a user profile data 422, a phrase data424, a category data 426, a product data 428, publication data 430, andrules data 432.

In an embodiment, the communication manager 412 receives a query tocreate a user profile or to acquire information about a user from thecustomer support center 110 through the transceiver 404. Thecommunication manager 412 includes various protocol stacks such as, butnot limited to, Transmission Control Protocol and Internet Protocol(TCP/IP), User Datagram Protocol (UDP), 2G, 3G or 4 G communicationprotocols. The communication manager 412 transmits and receives themessages/data through the transceiver 404 in accordance with suchprotocol stacks.

The search module 414 searches for the one or more electronicpublications of the user in the one or more online sources. Thesearching of the one or more electronic publications has been describedin step 202 (refer FIG. 1). In an embodiment, the search module 414utilizes various searching technologies such as web crawling orextracting content directly from predefined review web sites. Further,the search module 414 stores the one or more electronic publications inthe publication data 430.

The NLP module 112 extracts the one or more electronic publications fromthe publication data 430. Further, the NLP module 112 analyzes sentencesin the one or more electronic publications to extract the one or morerelevant phrases as described in step 204. In an embodiment, the NLPmodule 112 includes a natural language parser that extracts the one ormore phrases. In an embodiment, some examples of commonly known naturallanguage natural language parser includes, but not limited to, XeroxIncremental Parser (XIP), Stanford Parser, Natural Language Toolkit(NLTK) and the like. The NLP module 112 stores the one or more relevantphrases as the phrase data 424. Additionally, the NLP module 112classifies each of the one or more relevant phrases in the one or morecategories by applying the first set of rules as described in the step206. The NLP module 112 stores the classified phrases as the categorydata 426. Further, the NLP module 112 applies the second set of rules toeach of the classified phrases to extract the one or more terms asdescribed in the step 208. In an embodiment, the one or more terms areindicative of the information about the user.

The user profile manager 416 creates a user profile based on the one ormore terms determined by the NLP module 112 as described in the step210. Further, the user profile manager 416 stores the user profile asthe user profile data 422. In an embodiment, the user profile manager416 stores the user profile in the user database 106.

The product database manager 418 extracts the one or more productinformation based on the user profile. In an embodiment, the productdatabase manager 418 creates a SQL query based on the user profile toextract the one or more products. The product database manager 418stores the one or more product information as the product data 428.

The customer care manager 420 extracts the user profile from the userprofile data 422. Further, the customer care manager 420 extracts theone or more product information from the product data 428. Thereafter,the customer care manager 420 communicates the user profile and the oneor more product information to the customer support center 110 throughthe transceiver 404.

The disclosed methods and systems, as illustrated in the ongoingdescription or any of its components, may be embodied in the form of acomputer system. Typical examples of a computer system include ageneral-purpose computer, a programmed microprocessor, amicro-controller, a peripheral integrated circuit element, and otherdevices, or arrangements of devices that are capable of implementing thesteps that constitute the method of the disclosure.

The computer system comprises a computer, an input device, a displayunit and the Internet. The computer further comprises a microprocessor.The microprocessor is connected to a communication bus. The computeralso includes a memory. The memory may be Random Access Memory (RAM) orRead Only Memory (ROM). The computer system further comprises a storagedevice, which may be a hard-disk drive or a removable storage drive,such as, a floppy-disk drive, optical-disk drive, etc. The storagedevice may also be a means for loading computer programs or otherinstructions into the computer system. The computer system also includesa communication unit. The communication unit allows the computer toconnect to other databases and the Internet through an Input/output(I/O) interface, allowing the transfer as well as reception of data fromother databases. The communication unit may include a modem, an Ethernetcard, or other similar devices, which enable the computer system toconnect to databases and networks, such as, LAN, MAN, WAN, and theInternet. The computer system facilitates inputs from a user throughinput device, accessible to the system through an I/O interface.

The computer system executes a set of instructions that are stored inone or more storage elements, in order to process input data. Thestorage elements may also hold data or other information, as desired.The storage element may be in the form of an information source or aphysical memory element present in the processing machine.

The programmable or computer readable instructions may include variouscommands that instruct the processing machine to perform specific taskssuch as, steps that constitute the method of the disclosure. The methodand systems described can also be implemented using only softwareprogramming or using only hardware or by a varying combination of thetwo techniques. The disclosure is independent of the programminglanguage and the operating system used in the computers. Theinstructions for the disclosure can be written in all programminglanguages including, but not limited to, ‘C’, ‘C++’, ‘Visual C++’ and‘Visual Basic’. Further, the software may be in the form of a collectionof separate programs, a program module containing a larger program or aportion of a program module, as discussed in the ongoing description.The software may also include modular programming in the form ofobject-oriented programming. The processing of input data by theprocessing machine may be in response to user commands, results ofprevious processing, or a request made by another processing machine.The disclosure can also be implemented in all operating systems andplatforms including, but not limited to, ‘Unix’, DOS', ‘Android’,‘Symbian’, and ‘Linux’.

The programmable instructions can be stored and transmitted on acomputer-readable medium. The disclosure can also be embodied in acomputer program product comprising a computer-readable medium, or withany product capable of implementing the above methods and systems, orthe numerous possible variations thereof.

Various embodiments of the methods and systems for creating user profileusing natural language processing (NLP) techniques have been disclosed.However, it should be apparent to those skilled in the art that manymore modifications, besides those described, are possible withoutdeparting from the inventive concepts herein. The embodiments,therefore, are not to be restricted, except in the spirit of thedisclosure. Moreover, in interpreting the disclosure, all terms shouldbe understood in the broadest possible manner consistent with thecontext. In particular, the terms “comprises” and “comprising” should beinterpreted as referring to elements, components, or steps, in anon-exclusive manner, indicating that the referenced elements,components, or steps may be present, or utilized, or combined with otherelements, components, or steps that are not expressly referenced.

A person having ordinary skills in the art will appreciate that thesystem, modules, and sub-modules have been illustrated and explained toserve as examples and should not be considered limiting in any manner.It will be further appreciated that the variants of the above disclosedsystem elements, or modules and other features and functions, oralternatives thereof, may be combined to create many other differentsystems or applications.

Those skilled in the art will appreciate that any of the aforementionedsteps and/or system modules may be suitably replaced, reordered, orremoved, and additional steps and/or system modules may be inserted,depending on the needs of a particular application. In addition, thesystems of the aforementioned embodiments may be implemented using awide variety of suitable processes and system modules and is not limitedto any particular computer hardware, software, middleware, firmware,microcode, etc.

The claims can encompass embodiments for hardware, software, or acombination thereof.

It will be appreciated that variants of the above disclosed, and otherfeatures and functions or alternatives thereof, may be combined intomany other different systems or applications. Various presentlyunforeseen or unanticipated alternatives, modifications, variations, orimprovements therein may be subsequently made by those skilled in theart which are also intended to be encompassed by the following claims.

What is claimed is:
 1. A method of acquiring information about a user,the method comprising: classifying one or more phrases in one or morecategories at least partly on the basis of a period for which a producthas been used by the user, the user's experience with the product,preferences of the user, or needs of the user by applying one or morenatural language processing (NLP) techniques, wherein the one or morephrases are extractable from an electronic publication at least partlyon the basis of on a predefined set of verbs, a predefined set ofdomain-specific terms, and terms indicative of temporal information; andextracting one or more terms from the classified phrases, wherein theone or more terms are indicative of the information about the user. 2.The method of claim 1 further comprising creating a user profile basedon the one or more terms.
 3. The method of claim 2 further comprisingtransmitting the user profile to a customer support center.
 4. Themethod of claim 2 further comprising providing one or more servicesbased on the user profile, wherein the one or more services correspondsto a product support, product recommendation, and troubleshooting. 5.The method of claim 1 further comprising searching for the electronicpublication on one or more online sources.
 6. The method of claim 3,wherein the one or more online sources correspond to at least one of aproduct review website, a blog, a forum or an e-mail.
 7. The method ofclaim 1, wherein the one or more NLP techniques comprise wordtokenization, word lemmatization, part-of-speech tagging, Named Entitydetection, syntactic parsing.
 8. The method of claim 1, wherein theelectronic publication is creatable from a voice call by applying one ormore speech to text (STT) techniques, wherein the user initiates thevoice call to obtain information about a product.
 9. A method ofproviding one or more services to a user, the method comprising:classifying one or more phrases in one or more categories at leastpartly on the basis of a period for which a product has been used by theuser, the user's experience with the product, preferences of the user,or needs of the user by applying one or more natural language processing(NLP) techniques, wherein the one or more phrases are extractable froman electronic publication based, at least in part, on a predefined setof verbs, a predefined set of Domain-specific terms, and termsindicative of temporal information; extracting one or more terms formthe classified phrases, wherein the one or more terms are indicative ofthe information about the user; creating a user profile based on theclassified phrases; and providing the one or more services to the userbased on the user profile, wherein the one or more services correspondto product support, product recommendation, and troubleshooting.
 10. Themethod of claim 9 further comprising searching for the electronicpublication on one or more online sources.
 11. The method of claim 10,wherein the one or more online sources correspond to at least one of aproduct review website, a blog, a forum or an e-mail.
 12. The method ofclaim 9, wherein the one or more categories comprises user's expertise,user's needs, and user's preferences.
 13. The method of claim 9, whereinthe one or more NLP techniques comprise word tokenization, wordlemmatization, part-of-speech tagging, Named Entity detection, syntacticparsing.
 14. A system for creating a user profile, the systemcomprising: a search module configured to search for an electronicpublication on one or more online sources; a natural language processing(NLP) module configured to: extract one or more phrases from theelectronic publication based, at least in part, on a predefined set ofverbs, a predefined set of Domain-specific terms, and terms indicativeof temporal information; and classify the one or more phrases in one ormore categories at least partly on the basis of a period for which aproduct has been used by the user, the user's experience with theproduct, preferences of the user, or needs of the user; and a userprofile manager configured to create the user profile based on theclassified phrases.
 15. The system of claim 14, wherein the user profilemanager further configured to extract one or more terms form theclassified phrases, wherein the user profile is created based on the oneor more terms.
 16. The system of claim 14 further comprising a customercare manager configured to transmit the user profile to a customersupport center, wherein the customer support center provides one or moreservices based on the user profile.
 17. The system of claim 14, whereinthe NLP module comprises a natural language parser, wherein the parserextracts the one or more phrases.
 18. The system of claim 14, whereinthe one or more online sources correspond to at least one of a productreview website, a blog, a forum or an e-mail.
 19. The system of claim14, wherein the one or more NLP techniques comprise word tokenization,word lemmatization, part-of-speech tagging, Named Entity detection,syntactic parsing.